Interview - Developmental Milestone Project

How to Measure Parents’ Developmental Foci: Domains and Methodologies

Chi Zhang

the University of Manchester

Sunday, the 1^th of February, 2026

About me

PhD in Education, The University of Manchester (2021–Present);
From belief to practice: Understanding teachers’ pedagogic choices in China’s mathematics education
MSc in Research Methods with Education (Distinction, 2020–2021);
The association between mathematics teachers’ exclusivity belief and pedagogic practice
MA in Mathematics and Pedagogy, The Education University of Hong Kong (2019–2020)

Interview question

How can one assess parents’ developmental foci in a cross-cultural context? With developmental foci, we mean the domain (e.g. linguistic, social, physical, etc.) of a child’s development that is of particular importance to parents.
Think about two communities (could be Germany and Namibia, but also other contexts you have experience with) and formulate hypotheses regarding potential differences in developmental foci.

The basic IRT assumptions

Physical and social settings of the child’s daily life;
Customs and practices of care; and
The psychology of the caretakers (Super & Harkness, 1986).

Cultural models of parenting and child development held by parents and others (Harkness & Super, 1996);
Also link to parents’ cultural belief systems/expectations/goals/focus etc.
“Within the constraints imposed by the wider environment, parents make choices about the best ways to take care of their children, and these choices tend to follow culturally recognizable patterns (Harkness & Super, 2020, p. 18).”

Since IRT models carry a strong assumption that a set of items measure a single underlying construct; it’s important to first clarify what we mean by “developmental foci.” To frame this, I’ll draw on the concept of the developmental niche, which describes children’s development through three interrelated components, and Parents’ developmental foci can be understood as a core part of the third component. This connects closely to another framework i.e, parental ethnotheories, the cultural models of parenting. The ethnotheories shape parents’ beliefs, expectations, and goals for their children, and these all reflect what parents perceive as “the best ways” to raise their children, and these choices tend to follow certain cultural patterns. So my take away is: developmental foci are not accidental interests that are completely individualized, nor are they structural forces that one cannot escape. Rather, they represent parents’ internalized cultural models that can be observed and, crucially, measured.

The basic assumptions of cultural models

Unidimensional spectrum
Example

Individualism/collectivism (e.g., Triandis, 1989); distal/proximal parenting (Keller, 2009); autonomy/relatedness (Keller et al., 2006) …

+ : Easy to model (1|person);
- : Too simplified, criticisms argue autonomy and relatedness can coexist and need to coexist because they are both human needs (Keller, 2016; Oyserman et al., 2002).

Data from (Ndzenyuiy et al., 2026):

Parental Ethnotheory Questionnaire (PEQ) Item	Factor Loading
It is important to rock a crying baby on the arms in order to console him/her.	0.000
Sleeping through the night should be trained as early as possible.	-0.209
It is not necessary to react immediately to a crying baby.	-0.321
You cannot start early enough to direct the infant’s attention towards objects and toys.	-0.152
Gymnastics make a baby strong.	0.507
If a baby is fussy, he/she should be immediately picked up.	0.767
It is good for a baby to sleep alone.	0.000
When a baby cries, he/she should be narrated immediately.	0.551
Babies should be left crying for a moment in order to see whether they console themselves.	-0.175
A baby should be always in close proximity with his/her mother, so that she can react immediately to his/her signals.	0.424

Now, there are different assumptions about these cultural models. The convenient one is the unidimensional assumption. We have many binary pairs in the literature here. Under this assumption, a parent’s tendency falls somewhere on a single spectrum, you either be individualism or collectivism, you can’t be both. This makes modeling straightforward—we only need one set of items, either positively or negatively worded, and a simple random intercept for each person. However, this approach has received many criticism for being overly simplified. People can—and indeed must—hold both orientations often, because e.g., autonomy and relatedness are both fundamental human needs. My own critique is that this won’t work because parents can simultaneously expect their children to think independently while also maintaining close ties with family and community. This is increasingly common in China, for example. Roman, in your Cameroon study, you may have noticed that the factor analysis results show most negatively worded items are not functioning well. This also suggests they might be tapping into a different dimension of the construct, rather than simply the opposite side.

The basic assumptions of cultural models

Multidimensional construct
Example

For example, Kağıtçıbaşı’s (2017) four-field scheme with variations in the dimensions of agency (autonomy & heteronomy) and interpersonal distance (closeness & separateness).

independent self: autonomy + separateness (western middle-class, Germany)
interdependent self: heteronomy + closeness (rural farming community, Namibia)
autonomous-related self: autonomy + closeness (non-western urban community, China)
separated heteronomous self: heteronomy + separateness

How important is it that your child, when an adult (Liang et al., 2021)…

Item	Item (stem continuation)	Dimension
1	… maintains good relationships with many people?	Relatedness
2	… cares about others’ feelings?	Relatedness
3	… is loyal to his or her friends?	Relatedness
4	… feels well connected to other people?	Relatedness
5	… is well connected to the extended family (grandparents, aunts, cousins, etc.)?	Relatedness
6	… tries to reach his or her goals without anyone else’s help?	Autonomy
7	… tries not to depend on someone else to achieve his or her goals?	Autonomy
8	… typically decides on a course of action without help from others?	Autonomy
9	… makes decisions about what to do without being influenced by others’ opinions?	Autonomy
10	… likes to live without many ties to others?	Separateness
11	… prefers to live alone?	Separateness
12	… keeps personal issues to himself or herself?	Separateness
13	… does things in traditional ways?	Heteronomy
14	… does the things that other people expect of him or her?	Heteronomy
15	… avoids doing things that other people say are wrong?	Heteronomy

Some concerns

Issues in IRT

Optimistic/Conservative responding style
Ceiling effect
Anchoring

Now there are some practical concerns I can think of when applying IRT in cross-cultural research. The first is responding style. I once watched a Cambridge student arguing with Charlie Clark that when African respondents tick five on “how happy are you” and Western respondents tick three, these scores are not objectively, or cross-culturally, comparable. And if you look at the PISA 2012 example here, classroom management is positively linked to math performance within each country. But when you pool the data across countries, the correlation disappears, and it even becomes negative when you add more countries. One explanation is that lower-performing communities may have more optimistic responding styles. The second concern is ceiling effect. When asking about parental foci, respondents tend to tick extreme categories like one or five quite easily, which makes threshold estimation unstable. These concerns can, to some extent, be addressed by providing anchors in the research design.

Strange Results in PISA 2012

From (Kyllonen, 2015)

Example of anchors

Anchor in category (How often do you…. ‘half of the time’)
Anchor in items (items from free listing, Van de Vijver & Tanzer, 2004)
Anchor in context (We would like to know which of the following characteristics are important for your child when he or she has become an adult…; )
Anchor in modelling (Comm in fixed/random effect)

Simulation and visualisation

Simulation Settings (N=200)

Sample & Scale:
- Groups: Germany (g, \(n=100\)) | Namibia (n, \(n=100\)).
- Scale: 30 items (10 per domain), 4-point Likert, RSM (acat) logic.
Latent Structure (Traits):
- Domains: L (Linguistics), S (Social), P (Professional).
- Consistency: \(\rho = 0.6\) for same traits across domains.
- Conflicts (\(a\) vs \(r\)): \(L = -0.2\) | \(S = -0.5\) | \(P = -0.8\) (highest tension).
Group n (Namibia) Specifics:
- Trait Shift: Autonomy (a) -0.5; Relatedness (r) +0.5.
- Response Bias: Added systematic +0.6 “optimism bias”.
- Group g (Germany) Specifics:
- Trait Shift: Autonomy (a) +0.5; Relatedness (r) -0.5.

Simulation via `brms`

Simulated data example
Base model
Model summary

ID	comm	Domain	item	resp	dim_r
92	G	L	Lr5	3	1
101	N	P	Pr3	2	1
123	N	S	Sr5	4	1
140	N	S	Sr5	1	1
147	N	S	Sr3	4	1
162	N	S	Sr4	4	1

fit <- brm(
  formula = resp ~ 1 + comm + (0 + dim_a + dim_r | ID) + (1 | item),
  data = simdata,
  family = brmsfamily("acat", "logit"), 
  prior = c(
    prior(normal(0, 3), class = "b"), 
    prior(normal(0, 1), class = "sd"),
    prior(lkj(2), class = "cor")
  ),
  backend = "cmdstanr", chains = 4, iter = 2000, warmup = 1000, cores = 4,
  file = "models/fit_rsm"
)

 Family: acat 
  Links: mu = logit; disc = identity 
Formula: resp ~ 1 + comm + (0 + dim_a + dim_r | ID) + (1 | item) 
   Data: simdata (Number of observations: 6000) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Multilevel Hyperparameters:
~ID (Number of levels: 200) 
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(dim_a)            0.69      0.05     0.60     0.80 1.00     1426     2412
sd(dim_r)            0.68      0.05     0.58     0.79 1.00     1120     2257
cor(dim_a,dim_r)    -0.29      0.08    -0.43    -0.13 1.00      876     1712

~item (Number of levels: 30) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     0.72      0.10     0.55     0.93 1.01      791     1646

Regression Coefficients:
             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]    -0.91      0.15    -1.20    -0.61 1.01      347      590
Intercept[2]     0.07      0.15    -0.22     0.35 1.01      318      453
Intercept[3]     1.11      0.15     0.81     1.39 1.01      322      425
commN            0.60      0.08     0.44     0.77 1.00     1137     1867

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

Visualise the base model

Wright map
Group Comparison

**Figure 1: Wright map.** Results from the Bayesian 1PL RSM. The leftmost panel displays item difficulty estimates across the three domains (L, S, P). The two rightmost panels highlight the community-level trait differences.

**Figure 2: Community Effects.** Conditional effects of Community. The left plot shows the predicted mean response. The right plot displays category-wise probabilities (1–4), demonstrating how the **+0.6 optimism bias** in group \(N\) shifts responses toward higher categories.

Advance model specification

Advance model
Model summary

fit_sep <- brm(
 formula = resp ~ 1 + comm + (0 + Domain:dim_a + Domain:dim_r | ID) + (1 | item),
  data = simdata,
  family = brmsfamily("acat", "logit"), 
  prior = c(
    prior(normal(0, 1), class = "sd"),
    prior(lkj(2), class = "cor")
  ),
  chains = 4, iter = 2000, warmup = 1000, cores = 4, backend = "cmdstanr",
  file = "models/fit_sep_dim"
)

 Family: acat 
  Links: mu = logit; disc = identity 
Formula: resp ~ 1 + comm + (0 + Domain:dim_a + Domain:dim_r | ID) + (1 | item) 
   Data: simdata (Number of observations: 6000) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Multilevel Hyperparameters:
~ID (Number of levels: 200) 
                                 Estimate Est.Error l-95% CI u-95% CI Rhat
sd(DomainL:dim_a)                    0.86      0.07     0.72     1.02 1.00
sd(DomainP:dim_a)                    0.97      0.07     0.83     1.13 1.00
sd(DomainS:dim_a)                    0.97      0.08     0.83     1.13 1.00
sd(DomainL:dim_r)                    0.91      0.08     0.76     1.07 1.00
sd(DomainP:dim_r)                    1.08      0.08     0.92     1.25 1.00
sd(DomainS:dim_r)                    0.86      0.08     0.72     1.02 1.00
cor(DomainL:dim_a,DomainP:dim_a)     0.65      0.07     0.51     0.78 1.00
cor(DomainL:dim_a,DomainS:dim_a)     0.57      0.08     0.41     0.71 1.00
cor(DomainP:dim_a,DomainS:dim_a)     0.47      0.08     0.29     0.62 1.01
cor(DomainL:dim_a,DomainL:dim_r)    -0.21      0.09    -0.39    -0.03 1.00
cor(DomainP:dim_a,DomainL:dim_r)    -0.11      0.09    -0.29     0.08 1.01
cor(DomainS:dim_a,DomainL:dim_r)     0.06      0.10    -0.13     0.24 1.00
cor(DomainL:dim_a,DomainP:dim_r)    -0.11      0.10    -0.30     0.08 1.00
cor(DomainP:dim_a,DomainP:dim_r)    -0.61      0.07    -0.73    -0.46 1.00
cor(DomainS:dim_a,DomainP:dim_r)     0.03      0.09    -0.15     0.21 1.00
cor(DomainL:dim_r,DomainP:dim_r)     0.53      0.08     0.37     0.68 1.00
cor(DomainL:dim_a,DomainS:dim_r)    -0.10      0.10    -0.29     0.10 1.00
cor(DomainP:dim_a,DomainS:dim_r)    -0.10      0.10    -0.29     0.09 1.00
cor(DomainS:dim_a,DomainS:dim_r)    -0.47      0.08    -0.62    -0.31 1.00
cor(DomainL:dim_r,DomainS:dim_r)     0.62      0.08     0.46     0.75 1.00
cor(DomainP:dim_r,DomainS:dim_r)     0.35      0.09     0.17     0.52 1.00
                                 Bulk_ESS Tail_ESS
sd(DomainL:dim_a)                    1637     2807
sd(DomainP:dim_a)                    1513     2588
sd(DomainS:dim_a)                    1841     2533
sd(DomainL:dim_r)                    1992     2607
sd(DomainP:dim_r)                    1734     2899
sd(DomainS:dim_r)                    1626     2820
cor(DomainL:dim_a,DomainP:dim_a)      785     1539
cor(DomainL:dim_a,DomainS:dim_a)      898     1864
cor(DomainP:dim_a,DomainS:dim_a)      996     1846
cor(DomainL:dim_a,DomainL:dim_r)      749     1665
cor(DomainP:dim_a,DomainL:dim_r)      789     1749
cor(DomainS:dim_a,DomainL:dim_r)     1066     2013
cor(DomainL:dim_a,DomainP:dim_r)      796     1577
cor(DomainP:dim_a,DomainP:dim_r)     1353     2254
cor(DomainS:dim_a,DomainP:dim_r)     1541     2504
cor(DomainL:dim_r,DomainP:dim_r)     1857     3098
cor(DomainL:dim_a,DomainS:dim_r)      825     1572
cor(DomainP:dim_a,DomainS:dim_r)      951     1993
cor(DomainS:dim_a,DomainS:dim_r)     1166     2379
cor(DomainL:dim_r,DomainS:dim_r)     1996     2733
cor(DomainP:dim_r,DomainS:dim_r)     2076     2828

~item (Number of levels: 30) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     0.83      0.12     0.63     1.11 1.00      641     1242

Regression Coefficients:
             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]    -1.13      0.17    -1.48    -0.81 1.01      331      682
Intercept[2]     0.06      0.16    -0.28     0.38 1.01      314      708
Intercept[3]     1.35      0.17     1.00     1.66 1.01      346      724
commN            0.70      0.09     0.52     0.87 1.00      989     1790

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

Visualise advance model

Domain Correlations
Correlation Matrix

**Figure 3: Domain-specific latent correlations.** Scatter plots illustrating the relationship between Autonomy (\(a\)) and Relatedness (\(r\)) across Linguistics (L), Social (S), and Professional (P) domains. Dashed lines represent the linear regressions of the recovered core conflicts, while point shapes distinguish between the German (\(G\)) and Namibian (\(N\)) communities.

**Figure 4: Latent correlation matrix (**\(\Phi\)) recovery. Heatmaps comparing the posterior mean estimates (left) against the target truth values (right) for the \(6 \times 6\) latent structure. The model effectively recovers the simulated correlations.

References

Harkness, S., & Super, C. M. (1996). Parents’ cultural belief systems: Their origins, expressions, and consequences. Guilford Press.

Harkness, S., & Super, C. M. (2020). Why understanding culture is essential for supporting children and families. Applied Developmental Science, 25(1), 14–25. https://doi.org/10.1080/10888691.2020.1789354

Kagitcibasi, C. (2017). Family, Self, and Human Development Across Cultures: Theory and Applications. Routledge. https://doi.org/10.4324/9781315205281

Keller, H. (2009). Cultures of Infancy. The Foundation of Developmental Pathways. Papers from the International Association for Cross-Cultural Psychology Conferences. https://doi.org/<a href="https://doi.org/10.4087/OLVD6449">https://doi.org/10.4087/OLVD6449</a>

Keller, H. (2016). Psychological autonomy and hierarchical relatedness as organizers of developmental pathways. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1686), 20150070. https://doi.org/10.1098/rstb.2015.0070

Keller, H., Lamm, B., Abels, M., Yovsi, R., Borke, J., Jensen, H., Papaligoura, Z., Holub, C., Lo, W., Tomiyama, A. J., Su, Y., Wang, Y., & Chaudhary, N. (2006). Cultural Models, Socialization Goals, and Parenting Ethnotheories: A Multicultural Analysis. Journal of Cross-Cultural Psychology, 37(2), 155–172. https://doi.org/10.1177/0022022105284494

Kyllonen, P. (2015). Conference on measuring and assessing skills, chicago, IL.

Liang, Y., Tudge, J. R. H., Mokrova, I. L., Freitas, L. B. L., Merçon-Vargas, E. A., Mendonça, S. E., O’Brien, L., Kiang, L., Payir, A., Cao, H., & Zhou, N. (2021). Measuring parents’ developmental goals for their children: Updating Kağitçibaşi’s approach to autonomy-relatedness in the United States and China. Current Psychology, 40(10), 4791–4800. https://doi.org/10.1007/s12144-019-00421-8

Ndzenyuiy, M., Liebal, K., Stengelin, R., Stodulka, T., & Haun, D. B. M. (2026). Parenting culture: A multimethod perspective on parenting practices among the Nso, Cameroon. Developmental Psychology, 62(1), 65–75. https://doi.org/10.1037/dev0002044

Oyserman, D., Coon, H. M., & Kemmelmeier, M. (2002). Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychological Bulletin, 128(1), 3–72. https://doi.org/10.1037/0033-2909.128.1.3

Super, C. M., & Harkness, S. (1986). The developmental niche: A conceptualization at the interface of child and culture. International Journal of Behavioral Development, 9(4), 545–569. https://doi.org/10.1177/016502548600900409

Triandis, H. C. (1989). The self and social behavior in differing cultural contexts. Psychological Review, 96(3), 506.

Van de Vijver, F., & Tanzer, N. K. (2004). Bias and equivalence in cross-cultural assessment: An overview. European Review of Applied Psychology, 54(2), 119–135.

ID	comm	Domain	item	resp	dim_r
92	G	L	Lr5	3	1
101	N	P	Pr3	2	1
123	N	S	Sr5	4	1
140	N	S	Sr5	1	1
147	N	S	Sr3	4	1
162	N	S	Sr4	4	1

ID	comm	Domain	item	resp	dim_r
92	G	L	Lr5	3	1
101	N	P	Pr3	2	1
123	N	S	Sr5	4	1
140	N	S	Sr5	1	1
147	N	S	Sr3	4	1
162	N	S	Sr4	4	1

Interview - Developmental Milestone Project

About me

Interview question

The basic IRT assumptions

What is parents’ development foci?

The basic assumptions of cultural models

The basic assumptions of cultural models

Some concerns

Issues in IRT

Strange Results in PISA 2012

Example of anchors

Simulation and visualisation

Simulation Settings (N=200)

Simulation via brms

Visualise the base model

Advance model specification

Visualise advance model

References

Simulation via `brms`

ID	comm	Domain	item	resp	dim_r
92	G	L	Lr5	3	1
101	N	P	Pr3	2	1
123	N	S	Sr5	4	1
140	N	S	Sr5	1	1
147	N	S	Sr3	4	1
162	N	S	Sr4	4	1